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About this paper
Yeonghyeon Lee, Inmo Yeon, Juhan Nam, Joon Son Chung
VoiceLDM, developed at KAIST, is a generative audio model that synthesizes intelligible speech seamlessly integrated into diverse environmental contexts, guided by separate text prompts for content and description. The model achieves superior speech intelligibility, outperforming ground truth audio in certain metrics, while demonstrating competitive zero-shot text-to-audio capabilities.
Key insights
- Integrating a dual-prompt conditioning mechanism into a latent diffusion model effectively bridges the gap between text-to-speech and text-to-audio generation.
- Leveraging large-scale, unannotated real-world audio data with pre-trained self-supervised models like Whisper and CLAP enables robust training without extensive manual annotations.
- Dual classifier-free guidance provides a flexible and precise method for users to control the trade-off between linguistic accuracy and environmental contextualization in generated audio.
Summary by alphaXiv.
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